西安夏季大气亚微米颗粒物化学组成与来源的在线观测研究

西安作为汾渭平原地区最大的城市,大气颗粒物污染形势严峻.2017年夏季期间,在西安市浐灞生态园区运用气溶胶化学组分监测仪,对大气亚微米颗粒物中的非难挥发性组分(NR-PM1)进行了在线监测.观测期间NR-PM1的平均质量浓度为(30.1?±?15.4)μg???m?3.其中有机物含量最高,占NR-PM1总质量浓度的63...     

FORECASTING DRY SEASON STREAMFLOW ON THE PEACE RIVER AT ARCADIA,FLORIDA, USA1

ABSTRACT: The Peace River at Arcadia, Florida, is a municipal water supply supplement for southwestern Florida. Consequently, probabilities of encountering low flows during the dry season are of critical importance. Since the association between Pacific Ocean sea surface temperatures (SSTs) and seasonal streamflow variability in the southeastern United States is well documented, it is reasonable to generate forecasts based on this information. Here, employing historic records of minimum, mean, and maximum flows during winter (JFM) and spring (AMJ), upper and lower terciles define “above normal,”“normal,” and “below normal” levels of each variable. A probability distribution model describes the likelihood of these seasonal variables conditioned upon Pacific SSTs from the previous summer (JAS). Model calibration is based upon 40 (of 50) years of record employing stratified random sampling to ensure equal representation from each decade. The model is validated against the remaining 10 samples and the process repeated 100 times. Each conditional probability distribution yields varying probabilities of observing flow variables within defined categories. Generally, a warm (cold) Pacific is associated with higher (lower) flows. To test model skill, the forecast is constrained to be the most probable category in each calibration year, with significance tested by chi‐square frequency tables. For all variables, the tables indicate high levels of association between forecast and observed terciles and forecast skill, particularly during winter. During spring the pattern is less clear, possibly due to the variable starting date of the summer rainy season. This simple technique suggests that Pacific SSTs provide a good forecast of low flows.     

PREDICTION OF MEAN ANNUAL FLOWS IN NORTH AND CENTRAL FLORIDA1

ABSTRACT: A frequency analysis approach for the prediction of flow characteristics at ungaged locations is applied to a region of high annual precipitation and low topography in north and central Florida. Stationary time series of annual flows are fitted with the lognormal distribution and estimated parameters of the distribution are fitted by third order trend surfaces. These explain 65 and 74 percent of the observed variances in the mean and standard deviation, respectively. Predictions of parameters are then made for several locations previously unused in the study and they are used to estimate the return periods of various flows from the lognormal distribution. Application of the Kolmogorov-Smirnov goodness-of-fit test suggests that only one of the five test stations can be considered significantly different from the observed data, confirming the applicability of this technique.     

PREDICTION OF MEAN ANNUAL FLOWS IN THE FRASER RIVER CATCHMENT,BRITISH COLUMBIA1

ABSTRACT: A method of predicting annual flows is presented and is applied to the Fraser River catchment. Statistical tests show the annual flow records to be stationary and aerially independent and can be adequately approximated by Gaussian distributions. Estimates are made of the Gaussian parameters for each subbasin. The spatial variations of these parameters are described by third order trend surfaces. The fitted surfaces can then be used to predict parameters of ungaged basins using the latitude and longitude of the basin centroids. The predicted parametric values are substituted into the Gaussian distribution to generate flows of various return periods.     

Participatory scenario planning for developing innovation in community adaptation responses: three contrasting examples from Latin America

Environmental change requires adaptive responses that are innovative, forward-looking and anticipatory, in order to meet goals for sustainability in socio-ecological systems. This implies transformative shifts in understanding as conceptualised by the idea of ‘double’- or ‘triple-loop learning’. Achieving this can be difficult as communities often rely on shorter-term coping mechanisms that purport to maintain the status quo. The use of participatory scenario planning to stimulate forward-looking social learning for adaptation was investigated through three contrasting community case studies on natural resource management in Latin America (in Mexico, Argentina and Colombia). Exploratory scenario narratives that synthesised local knowledge and future perceptions were used iteratively to define response options considered robust across multiple futures. Despite its intensive format, participants in each case agreed that scenario planning enabled a more systematic appraisal of the future. Scenarios facilitated innovation by providing scope to propose new types of responses and associated actions. Differences in local context meant that learning about future change developed in diverse ways, showing a need for a reflexive process. Reframing of key issues characteristic of double-loop learning did occur, albeit through different forms of interaction in each location. However, a shift towards transformative actions characteristic of triple-loop learning was less evident. Achieving this would appear to require ongoing use of scenarios to challenge social norms in light of changing drivers. Use of learning loops as a diagnostic to evaluate adaptive responses provided a useful reference framework although in practice both innovation and consolidative approaches can develop concurrently for responses to different issues.     

Changes in vegetation persistence across global savanna landscapes, 1982–2010

We present a global analysis of the changing face of vegetation persistence in savanna ecosystems by boreal seasons. We utilized nearly 30 years of monthly normalized difference vegetation index data in an innovative time-series approach and developed associated statistical significance tests, making the application of continuous vegetation metrics both more rigorous and more useful to research. We found that 8,000,000–11,000,000 km² of savanna have experienced significant vegetation decline during each season, while 20,000,000–23,000,000 km² have experienced an increase in vegetation persistence during each season, relative to the baseline period (1982–1985). In addition, with the exception of the March–April–May season, which is mixed, the pattern of significant vegetation persistence in the Northern Hemisphere is almost exclusively positive, while it is negative in the Southern Hemisphere. This finding highlights the increasing vulnerability of the Southern Hemisphere savanna landscapes; either resulting from changing precipitation regimes (e.g., southern Africa) or agricultural pressures and conversions (e.g., South America).     

REGIONALIZATION AND PREDICTION OF ANNUAL FLOODS IN THE FRASER RIVER CATCHMENT,BRITISH COLUMBIA1

A method of predicting probability distributions of annual floods is presented and is applied to the Fraser River catchment of British Columbia. The Gumbel distribution is found to adequately describe the observed flood frequency data. Using the estimated Gumbel parameters, discriminant analysis is performed to separate basins into flood regions. Within each region, regression analysis is used to relate physiographic and climatic variables to the means and standard deviations of the annual flood series. The regression equations are applied to four test basins and the results indicate that the method is suitable for an estimation of annual floods.     

REGIONALIZATION AND PREDICTION OF FLOODS IN THE FRASER RIVER CATCHMENT,B.C.1

Probability distributions that model the return periods of flood characteristics derived from partial duration series are proposed and tested in the Fraser River catchment of British Columbia. Theoretical distributions describing the magnitude, duration, frequency and timing of floods are found to provide a goof fit to the observed data. The five estimated parameters summarizing the flood characteristics of each basin are entered into a discriminant analysis procedure to establish flood regions. Three regions were identified, each displaying flood behavior closely related to the physical conditions of the catchment. Within each region, regression equations are obtained between parameter values and basin climatic and physiographic variables. These equations provide a satisfactory prediction of flood parameters and this allows the estimation of a comprehensive set of flood characteristics for areas with sparse hydrologic information.